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import torch |
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from torchio import RandomGamma |
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from ...utils import TorchioTestCase |
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class TestRandomGamma(TorchioTestCase): |
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"""Tests for `RandomGamma`.""" |
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def test_with_zero_gamma(self): |
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transform = RandomGamma(log_gamma=0) |
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transformed = transform(self.sample) |
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self.assertTensorAlmostEqual(self.sample.t1.data, transformed.t1.data) |
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def test_with_non_zero_gamma(self): |
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transform = RandomGamma(log_gamma=(0.1, 0.3)) |
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transformed = transform(self.sample) |
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self.assertTensorNotEqual(self.sample.t1.data, transformed.t1.data) |
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def test_with_high_gamma(self): |
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transform = RandomGamma(log_gamma=(100, 100)) |
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transformed = transform(self.sample) |
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self.assertTensorAlmostEqual( |
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self.sample.t1.data == 1, transformed.t1.data |
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) |
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def test_with_low_gamma(self): |
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transform = RandomGamma(log_gamma=(-100, -100)) |
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transformed = transform(self.sample) |
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self.assertTensorAlmostEqual( |
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self.sample.t1.data > 0, transformed.t1.data |
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) |
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def test_wrong_gamma_type(self): |
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with self.assertRaises(ValueError): |
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RandomGamma(log_gamma='wrong') |
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def test_negative_values(self): |
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with self.assertWarns(UserWarning): |
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RandomGamma()(torch.rand(1, 3, 3, 3) - 1) |
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fepegar /
torchio
